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Python for Probability, Statistics, and Machine Learning by J... - 9783031046476
Python for Probability, Statistics, and Machine Learning by J... - 9783031046476
Python for Probability, Statistics, and Machine Learning by J... - 9783031046476
Python for Probability, Statistics, and Machine Learning by J... - 9783031046476
Python for Probability, Statistics, and Machine Learning by J... - 9783031046476

Python for Probability, Statistics, and Machine Learning by J... - 9783031046476

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Python for Probability, Statistics, and Machine LearningAuthor(s): Jos Unpingco\nFormat: Hardback\nPublisher: Springer International Publishing AG, Switzerland\nImprint: Springer International Publishing AG\nISBN-13: 9783031046476, 978-3031046476\nSynopsis\nUsing a novel integration of mathematics and Python codes, this book illustrates the fundamental concepts that link probability, statistics, and machine learning, so that the reader can not only employ statistical and machine learning models using modern Python modules, but also understand their relative strengths and weaknesses. To clearly connect theoretical concepts to practical implementations, the author provides many worked-out examples along with \""Programming Tips\"" that encourage the reader to write quality Python code. The entire text, including all the figures and numerical results, is reproducible using the Python codes provided, thus enabling readers to follow along by experimenting with the same code on their own com.

Python for Probability, Statistics, and Machine Learning by J... - 9783031046476

Python for Probability, Statistics, and Machine LearningAuthor(s): Jos Unpingco\nFormat: Hardback\nPublisher: Springer International Publishing AG, Switzerland\nImprint: Springer International Publishing AG\nISBN-13: 9783031046476, 978-3031046476\nSynopsis\nUsing a novel integration of mathematics and Python codes, this book illustrates the fundamental concepts that link probability, statistics, and machine learning, so that the reader can not only employ statistical and machine learning models using modern Python modules, but also understand their relative strengths and weaknesses. To clearly connect theoretical concepts to practical implementations, the author provides many worked-out examples along with \""Programming Tips\"" that encourage the reader to write quality Python code. The entire text, including all the figures and numerical results, is reproducible using the Python codes provided, thus enabling readers to follow along by experimenting with the same code on their own com.

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